2025 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Progressive Advanced Data Science and Artificial Intelligence 2
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Ryutaro Ichise / Sergei Manzhos / Rios De Sousa Arthur Matsuo Yamashita / Katsumi Nitta / Keiji Okumura / Isao Ono / Yoshihiro Miyake
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- DSA.A602
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 4Q
- Syllabus updated
- Mar 19, 2025
- Language
- English
Syllabus
Course overview and goals
Today, utilization of computation and data is required in various fields. In this course, we teach methods for analyzing and utilizing data using computers, which are important to be active as researchers and engineers in science and engineering. The course covers advanced topics that are not covered in the courses of Fundamentals of Data Science and Fundamentals of Progressive Data Science.
Course description and aims
The goal is to understand how to use computers to analyze and utilize data.
Keywords
Bayesian Network (Probabilistic Inference Model)，Variational Bayesian Method，anomaly detection，anomaly detection，Simulation，Knowledge Graphs
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
ZOOM is used to allow students to take courses at Ookayama or Suzukakedai campuses.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Bayesian network (Probabilistic Inference Model) | Understanding mechanisms for constructing Bayesian networks (probabilistic models) from big data and probabilistic inference algorithms for prediction and simulation of real-world phenomena. |
| Class 2 | Variational Bayesian Method | Understanding the variational Bayesian algorithm and its application to DNN. |
| Class 3 | Time Series Analysis | Understanding methods for analyzing changes and patterns in data over time. |
| Class 4 | Anomaly Detection | Understanding the methods used to automatically identify anomalous behavior in a dataset that deviates from normal behavior. |
| Class 5 | Simulation and AI | Understanding methods and examples of the fusion of simulation and AI. |
| Class 6 | Knowledge Graphs | Understanding of knowledge graphs and their applications. |
| Class 7 | Application of Data Science and Artificial Intelligence Techniques to Frontier Research | Understanding applications of machine learning and data-based techniques in physical sciences and renewable energy technologies, including materials informatics for the discovery of new functional materials, machine learning improvement of modeling methods, and ML-assisted renewable energy system management. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None
Reference books, course materials, etc.
Distributed electronically at Science Tokyo LMS
Evaluation methods and criteria
Evaluation is based on in-class assignments and reports, and advanced assignment reports.
Related courses
- Fundamentals of progressive data science（XCO.T677）
- Exercises in fundamentals of progressive data science（XCO.T678）
- Fundamentals of progressive artificial intelligence（XCO.T679）
- Exercises in fundamentals of progressive artificial intelligence（XCO.T680）
Prerequisites
- This course is intended for doctoral students. Master's students should take Advanced Data Science and AI II (DSA.A502).
- Students should have basic knowledge of linear algebra, differential and integral calculus, and mathematical statistics.
- Students should be able to understand the content taught in Fundamentals of Data Science or Fundamentals of Progressive Data Science, as well as in Exercises in Fundamentals of Data Science or Exercises in Fundamentals of Progressive Data Science.